Modern three-dimensional imaging techniques, such as for example computerised tomography (CT) or magnetic resonance imaging (MRI), have the ability to produce volumetric representations of anatomy allowing users to examine acquired data retrospectively or under live screening from any plane and to apply image processing techniques to achieve accurate viewing of individual structures.
Such three-dimensional techniques produce large three-dimensional volume data sets comprising a three-dimensional array of voxels each representing a property of a corresponding measurement volume. In the case of CT data sets, each voxel usually represents the attenuation of X-ray radiation by a respective, corresponding measurement volume.
Usually each volume data set will contain data representative of various features that are extraneous to the patient or the subject under consideration, for example data representative of a table on which the patient is positioned whilst measurements are performed. The presence of data extraneous to the patient or other subject under consideration can interfere with subsequent algorithms or processes that are applied to the data.
For example, there are situations where a fully rotatable volume rendered view of a portion of a patient's anatomy is the most useful representation of the pathology, rather than a traditional radiological slab. A wide variety of such volume rendered views can be used for different purposes, for example as angiography presets. The volume rendered views may be based, for example, on volumetric MIPs. Since in many cases routine anatomy may interfere with viewing of the volume rendered anatomy, a facility may be needed to eliminate from view such extraneous portions of the patient. Furthermore, when viewing a 3D volume rendered view there are situations where the CT table is included in the scanned field of view and must be removed in order to visualize the underlying anatomy. In some systems, the user is required to manually select and delete the table from the visual representation of the 3D volume, piece by piece. It can be difficult to select only the table for deletion, without the selection encompassing the surrounding tissues. This process can be time consuming and require more user interaction than is desirable, particularly as a skilled person such as a radiologist is usually required to perform the manual selection.
It is also known to apply automatic registration or feature extraction procedures to identify and extract particular anatomical features. The presence of data representative of extraneous features can interfere with such procedures. The presence of image data representative of a table in CT studies can particularly influence registration or other image analysis procedures. The high-density portions of the table are highly uniform and thus tend to significantly affect the outcome of registration. Limiting registration to the domain of the body can improve results of the registration procedure. It may also improve bone segmentation and other algorithms. Therefore, it may be desirable to identify and extract data representative of a patient or other subject, and to discard data representative of extraneous features, before performing further processes.
A known technique for identifying and extracting data representative of a patient, and discarding data representative of a table, uses connected component analysis. At the first stage of the process expected intensity levels that may be obtained from a table and that may be obtained from a patient are each estimated. The estimates are then used to locate general areas in the image volume occupied by the table or occupied by the patient. Connected component analysis is then used to determine contiguous blocks of image data representative of the patient. The blocks of data representative of a patient, based upon intensity level, are retained and other data, such as data representative of the table, is suppressed.
The method described in the preceding paragraph relies on accurately selecting expected intensity levels both for a table or other extraneous feature and for a patient. A major issue with the method is that it assumes that there is an intensity threshold that separates the table from the body. Actual intensity levels for a particular table or patient often do not match expected levels, and thus data may be erroneously retained or discarded. In addition, there may well be further extraneous features such as clothing, head rests, pipes or tubing that may produce measurement intensities that do not match well with the expected intensities, again leading to data being erroneously discarded or retained.
In another known method, CT data representative of solid or liquid material is pre-selected based upon measured intensity levels, and data representative of air or other gas is discarded. Connected component analysis is used to identify different connected regions. Regions are then discarded as representing the CT table based upon their position in the volume. For example, if the patient was lying on a table located towards the bottom of a measurement volume then connected regions of the measurements data set corresponding to positions towards the bottom of the volume would be discarded. However, such methods rely on accurate knowledge of the position of the CT table relative to the measurement apparatus, which can vary from measurement to measurement. Furthermore, small inaccuracies of estimation of the position of the CT table can lead to data erroneously being retained or discarded. The failure rates of some such known methods have been estimated at around 60%.